DETAILED ACTION The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Claims 1-24 are pending for examination. Specification The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed. Claim objections Claims 5, 13 and 21 are objected to because of the following informalities: In claims 5, 13 and 21, line 2(line# refers to claim 5), it recites abbreviations “Cloud IT migration project”. Appropriate corrections are required. Claim Rejections - 35 USC § 101 35 U.S.C. 101 reads as follows: Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title. Claim 9-16 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. With regard to claims 9-16, the claims is drawn to a computer program product claim that including " a computer readable medium ". The specification recites at ¶ [0091] “ computer-readable medium may be any medium that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. ” Thus, applying the broadest reasonable interpretation in light of the claimed language, the “computer readable medium” taking into account the meaning of the words in their ordinary usage as they would be understood by one of ordinary skill in the art (MPEP 2111), that the claim as a whole covers both transitory and non-transitory medium. A transitory medium does not fall into any of the 4 categories of invention (process, machine, manufacture, or composition of matter). Also see MPEP 2106.03 (I) “ Even when a product has a physical or tangible form , it may not fall within a statutory category . For instance, a transitory signal, while physical and real, does not possess concrete structure that would qualify as a device or part under the definition of a machine, is not a tangible article or commodity under the definition of a manufacture (even though it is man-made and physical in that it exists in the real world and has tangible causes and effects), and is not composed of matter such that it would qualify as a composition of matter. Nuijten, 500 F.3d at 1356-1357, 84 USPQ2d at 1501-03. As such, a transitory, propagating signal does not fall within any statutory category. Mentor Graphics Corp. v. EVE-USA, Inc., 851 F.3d 1275, 1294, 112 USPQ2d 1120, 1133 (Fed. Cir. 2017); Nuijten, 500 F.3d at 1356-1357, 84 USPQ2d at 1501-03” . Applicant is advised to amend the claim to read either -- non-transitory computer readable medium -- or -- non-transitory computer readable media--. Claims 1-24 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Claim 1 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Step 1, Statutory Category : Yes , the claim 1 is a computer-implemented method that recites a series of steps and therefore falls in the statutory category of a process. Step 2A- Prong 1: Judicial Exception Recited : Yes , the claim recites: “ generating a complexity prediction for a current migration project using a complexity prediction model; defining an actual complexity for the effectuated migration project; comparing the actual complexity of the effectuated migration project to the complexity prediction of the current migration project to identify a complexity delta; and revising the complexity prediction model based, at least in part, upon the complexity delta. ” As drafted, the claim as a whole recites a method including steps that could be performed in the human mind, but for the recitation of generic computing components. The human mind can easily judging/evaluating/determining/generating a complexity prediction for a current migration project using a complexity prediction model (i.e., using a complexity prediction model is just a mathematical calculation for predication the complexity), defining/determining an actual complexity for the effectuated migration project (i.e., based on the observation), comparing/determining the actual complexity of the effectuated migration project to the complexity prediction of the current migration project to identify/determining a complexity delta, and revising/changing/designing/modifying the complexity prediction model based, at least in part, upon the complexity delta. Therefore, but for the recitation of generic computing components, these steps may be a Mental Processes that can be performed in the human mind (including an observation, evaluation, judgment, opinion). Therefore, yes, the claims do recite judicial exceptions. Step 2A- Prong 2: Integrated into a practical Application: No , this judicial exception is not integrated into a practical application. In particular, the claim recites an additional limitations that “ a computer-implemented method executed on a computer device” which is directed to Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a generic computer as a tool to perform an abstract idea (see MPEP 2106.05(f)). Further, the limitation of “effectuating the current migration project, thus resulting in an effectuated migration project” which is merely applying the judicial exception or abstract idea (See MPEP 2106.05(f)). The claim does not define any particular machine to “effectuating” other than a generic machine such as the “computer device,” and no details what so ever on how the claimed function will occur. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they not impose any meaningful limits on practicing the abstract idea. Therefore, the claim is directed to the abstract idea. Step 2B: Claim provides an Inventive Concept: No. The additional element “ a computer-implemented method executed on a computer device” which is directed to Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a generic computer as a tool to perform an abstract idea (see MPEP 2106.05(f)). Further, the limitation of “effectuating the current migration project, thus resulting in an effectuated migration project” which is merely applying the judicial exception or abstract idea (See MPEP 2106.05(f)). The claim does not define any particular machine to “effectuating” other than a generic machine such as the “computer device,” and no details what so ever on how the claimed function will occur. These additional elements and combination of the elements does not amount to significant more than the exception itself or provide an inventive concept in Step 2B. For these reasons, there is no inventive concept in the claim, and thus the claim is ineligible . Independent claims 9 and 17 are rejected for the same reason as claim 1 above. Claim 9 further recites “A computer program product residing on a computer readable medium having a plurality of instructions stored thereon which, when executed by a processor, cause the processor to perform operations”. Claim 17 further recites “A computing system including a processor and memory configured to perform operations comprising”. These additional elements are directed to generic computing components/functions merely applying the abstract idea (MPEP § 2106.05(f)) . With respect to the dependent claim 2, the claim elaborates that wherein the complexity prediction model is based, at least in part, upon prior-completed migration projects (“wherein the complexity prediction model is based, at least in part, upon prior-completed migration projects” are directed to Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)). With respect to the dependent claim 3, the claim elaborates that wherein the complexity prediction model is generated by processing completion information associated with the prior-completed migration projects (“the complexity prediction model is generated by processing completion information” are being treated as part of abstract idea and is analogous to Mental processes, such that concept can be performed in the human mind. Further, the claim as a whole is a Mental Processes that can be performed in the human mind (including an observation, evaluation, judgment, opinion)). With respect to the dependent claim 4, the claim elaborates that wherein revising the complexity prediction model based, at least in part, upon the complexity delta includes: utilizing the complexity delta as training data for the complexity prediction model (“utilizing the complexity delta as training data for the complexity prediction model” are directed to Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)). With respect to the dependent claim 5, the claim elaborates that wherein the current migration project includes an on-premise to cloud IT migration project (“wherein the current migration project includes an on-premise to cloud IT migration project” are directed to Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)). With respect to the dependent claim 6, the claim elaborates that wherein the complexity prediction concerns one or more of: a complexity score assigned to each of the one or more migration portions of the current migration project, thus defining one or more complexity scores; and a project index assigned to the current migration project that defines the relative complexity of the current migration project based, at least in part, upon the one or more complexity scores (“a complexity score assigned to each of the one or more migration portions of the current migration project, thus defining one or more complexity scores; and a project index assigned to the current migration project that defines the relative complexity of the current migration project based, at least in part, upon the one or more complexity scores” are being treated as part of abstract idea and is analogous to Mental processes, such that concept can be performed in the human mind. Further, the claim as a whole is a Mental Processes that can be performed in the human mind (including an observation, evaluation, judgment, opinion)). With respect to the dependent claim 7, the claim elaborates that wherein each of the plurality of migration portions concerns one or more of: an application migration task; a data migration task; and a general migration task (these limitations are directed to Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)). With respect to the dependent claim 8, the claim elaborates that wherein the project index has a normal value of one and the deviation of the project index above / below the normal value of one is indicative of the increased / decreased level of complexity of the current migration project with respect to a normal migration project (these limitations are being treated as part of abstract idea and is analogous to Mental processes, such that concept can be performed in the human mind. Further, the claim as a whole is a Mental Processes that can be performed in the human mind (including an observation, evaluation, judgment, opinion)). Dependent claims 10-16 recite the same features as applied to claims 2-8 respectively above, therefore they are also rejected under the same rationale. Dependent claims 18-24 recite the same features as applied to claims 2-8 respectively above, therefore they are also rejected under the same rationale. Claim Rejections - 35 USC § 112(b) The following is a quotation of 35 U.S.C. 112(b): (b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention. Claims 6-8, 14-16 and 22-24 are rejected under 35 U.S.C. 112(b), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention. As per claims 6, 14 and 22 (line# refers to claim 6): Line 6, it recites “ the relative complexity” lacks antecedence basis . In addition, the term “ relative complexity ” is a relative term which renders the claim indefinite. The term “ relative complexity ” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. As per claims 7-8, 15-16 and 23-24: They are computer-implemented method, computer program product and computing system claims that depend from rejected claims and do not resolve the deficiencies thereof and are therefore rejected for the same reasons as above. Claim Rejections - 35 USC § 103 The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action: A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made. Claims 1, 4-7, 9, 12-15, 17 and 20-23 are rejected under 35 U.S.C. 103 as being unpatentable over BAFNA et al. (US Pub. 2024/0045831 A1) in view of Ortiz et al. (US Pub. 2010/0146489 A1) and further in view of KARRI et al. (US Pub. 2023/0071278 A1). As per claim 1, BAFNA teaches the invention substantially as claimed including A computer-implemented method, executed on a computer device (BAFNA, Fig. 4, 400 computer device) , comprising: generating a complexity prediction for a current migration project using a complexity prediction model (BAFNA, [0033] lines 1-13, performing the one or more actions includes the migration system determining complexities associated with migrating the system based on the migration actions and generating a migration roadmap based on the complexities. For example, the migration system may populate a template with the determined complexities (e.g., determined by the Q-matrix model ) associated with migrating the system; also see [0002] lines 18-19, migrating the system to the cloud computing environment) ; effectuating the current migration project, thus resulting in an effectuated migration project (BAFNA, [0074] lines 3-13, to determine migration actions for migrating the system to the cloud computing environment (block 570). For example, the device may process the labelled utilization features, the cloud architecture, and the cost, with a Q-matrix model, to determine migration actions for migrating the system to the cloud computing environment ; also see [0075] lines 16-22, determining complexities associated with migrating the system based on the migration actions and generating a migration roadmap based on the complexities, or determining data groups of the system based on the migration actions and migrating data of the system to the cloud computing environment based on the data groups); revising the complexity prediction model based, at least in part, upon the migration action (BAFNA, [0075] lines 3-16, the device may perform one or more actions based on the migration actions, as described above. In some implementations, performing the one or more actions includes providing the migration actions for display, receiving a change to one of the migration actions and modifying the one of the migration actions based on the change, or retraining the machine learning model based on the migration actions (as including revising the complexity prediction model). In some implementations, performing the one or more actions includes generating a sizing template based on the migration actions, and creating an architecture for the cloud computing environment based on the sizing template). Although BAFNA teaches complexity prediction of the current migration project and effectuating the current migration project with retaining the machine learning model, BAFNA fails to explicitly teach defining an actual complexity for the effectuated migration project and comparing the actual complexity of the effectuated migration project to the complexity prediction of the current migration project to identify a complexity delta. However, Ortiz teaches defining an actual complexity for the effectuated migration project and comparing the actual complexity of the effectuated migration project to the complexity prediction of the current migration project to identify a complexity delta ( Ortiz, Abstract, lines 6-9, comparing anticipated performance results of the test software with actual results of the test software; determining whether there are differences in the anticipated performance and the actual performance; (as complexity delta based on comparing the actual complexity/performance to the complexity predication/anticipated performance ). It would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to have combined the teaching of BAFNA with Ortiz because Ortiz’s teaching of comparing the difference between actual performance with the anticipated performance would have provided BAFNA’s system with the advantage and capability to allow the system to easily identifying the performance difference between current action and anticipated performance in order to increasing the quality of software components and application which improving the system performance and efficiency (see Ortiz, [0007] “ increased quality of software components and application ”). BAFNA and Ortiz fail to specifically teach when revising the complexity prediction model, it is based, at least in part, upon the complexity delta. However, KARRI teaches when revising the complexity prediction model, it is based, at least in part, upon the complexity delta ( KARRI, [0042] lines 1-14, the difference of the estimated time 308 and the realized time 312 for execution path i exceeds the acceptable margin of error, and if (at block 610) the realized time 312 is less than the estimated time 308, i.e., the performance exceeded expectations for the execution path i, then control proceeds to block 608 to train the resource allocation MLM 122 to produce the determined resource allocation 310 with a high confidence level). It would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to have combined the teaching of BAFNA and Ortiz with KARRI because KARRI’s teaching of training the machine learning model based on the difference of the estimated time and the realized time (i.e., actual VS predication) would have provided BAFNA and Ortiz ’s system with the advantage and capability to improving the accuracy of the machine learning model for future predication in order to improving the system performance and efficiency. As per claim 4, BAFNA, Ortiz and KARRI teach the invention according to claim 1 above. BAFNA further teaches wherein revising the complexity prediction model based, at least in part, upon the migration action includes: utilizing the migration action as training data for the complexity prediction model (BAFNA, [0075] lines 3-16, the device may perform one or more actions based on the migration actions, as described above. In some implementations, performing the one or more actions includes providing the migration actions for display, receiving a change to one of the migration actions and modifying the one of the migration actions based on the change, or retraining the machine learning model based on the migration actions (as including revising the complexity prediction model). In some implementations, performing the one or more actions includes generating a sizing template based on the migration actions, and creating an architecture for the cloud computing environment based on the sizing template) . In addition, Ortiz teaches complexity delta ( Ortiz, Abstract, lines 6- 9, comparing anticipated performance results of the test software with actual results of the test software; determining whether there are differences in the anticipated performance and the actual performance; (as complexity delta based on comparing the actual complexity/performance to the complexity predication/anticipated performance). Further, KARRI teaches when revising the complexity prediction model, it is based on complexity delta ( KARRI, [0042] lines 1-14, the difference of the estimated time 308 and the realized time 312 for execution path i exceeds the acceptable margin of error, and if (at block 610) the realized time 312 is less than the estimated time 308, i.e., the performance exceeded expectations for the execution path i, then control proceeds to block 608 to train the resource allocation MLM 122 to produce the determined resource allocation 310 with a high confidence level). As per claim 5, BAFNA, Ortiz and KARRI teach the invention according to claim 1 above. BAFNA further teaches wherein the current migration project includes an on-premise to cloud IT migration project (BAFNA, [0002] lines 18-19, migrating the system to the cloud computing environment ; also see [0011] The system may have been executing for years, may have no centralized governance, and may include an outdated infrastructure with resources requiring various skillsets resulting in high capital and operational expenditures; [0037] spending time and money on migration of a non-functional system to the cloud computing environment, attempting and failing to migrate the system to the cloud computing environment). As per claim 6, BAFNA, Ortiz and KARRI teach the invention according to claim 1 above. BAFNA further teaches wherein the complexity prediction concerns one or more of : a complexity score assigned to each of the one or more migration portions of the current migration project, thus defining one or more complexity scores; and a project index assigned to the current migration project that defines the relative complexity of the current migration project based, at least in part, upon the one or more complexity scores (BAFNA, [0029] For example, when processing the labelled utilization features, the cloud architecture, and the cost, with the Q-matrix model, to determine the migration actions, the migration system may assign complexity scores to the labelled utilization features (e.g., system files ) (as each of the one or more migration portions) based on the cloud architecture and the cost, and may assign actions based on the complexity scores assigned to the labelled utilization features. The migration system may create a reward matrix based on the complexity scores assigned to the labelled utilization features and based on the assigned actions). As per claim 7, BAFNA, Ortiz and KARRI teach the invention according to claim 6 above. BAFNA further teaches wherein each of the plurality of migration portions concerns one or more of : an application migration task; a data migration task; and a general migration task (BAFNA, [0034] The migration system may automatically or semi-automatically migrate the data of the system to the cloud computing environment (as a data migration task)). As per claim 9, it is a computer program product claim of claim 1 above. Therefore, it is rejected for the same reason as claim 1 above. As per claims 12-15, they are computer program product claims of claims 4-7 respectively above. Therefore, they are rejected for the same reasons as claims 4-7 respectively above. As per claim 17, it is a computing system claim of claim 1 above. Therefore, it is rejected for the same reason as claim 1 above. As per claims 20-23, they are computing system claims of claims 4-7 respectively above. Therefore, they are rejected for the same reasons as claims 4-7 respectively above. Claims 2-3, 10-11 and 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over BAFNA , Ortiz and KARRI, as applied to claims 1, 9 and 17 respectively above, and further in view of Malik et al. (US Patent. 11,615,061 B1). As per claim 2, BAFNA, Ortiz and KARRI teach the invention according to claim 1 above. BAFNA, Ortiz and KARRI fail to specifically teach wherein the complexity prediction model is based, at least in part, upon prior-completed migration projects. However, Malik teaches wherein the complexity prediction model is based, at least in part, upon prior-completed migration projects ( Malik, Fig. 12, 1220, 1230; Col 14, lines 55-67, The evaluation of the workload may be performed in different ways. In some embodiments, a workload type may be identified for a database and client application (e.g., as different client applications of a same database may have different workload types. For example, features of the workload may be extracted from workload data and compared with a classification model generated for evaluating workload (e.g., by various machine learning techniques trained upon previous data migration , workload features, complexity , cost, or effort indications to perform the data migration , success or failure of the data migration , etc; Col 15, lines 13-15, the migration recommendation could include a scale of increasing difficulty, complexity , cost, or other factor (or combination of factors). It would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to have combined the teaching of BAFNA, Ortiz and KARRI with Malik because Malik’s teaching of using the model for evaluating and recommendation based on the previous migration would have provided BAFNA, Ortiz and KARRI’s system with the advantage and capability to allow the system to recommending the migration based on the previous migration status in order to improving the system performance and efficiency. As per claim 3, BAFNA, Ortiz, KARRI and Malik teach the invention according to claim 2 above. Malik further teaches wherein the complexity prediction model is generated by processing completion information associated with the prior- completed migration projects (Malik, Col 14, lines 60-67, features of the workload may be extracted from workload data and compared with a classification model generated for evaluating workload (e.g., by various machine learning techniques trained upon previous data migration , workload features, complexity , cost, or effort indications to perform the data migration , success or failure of the data migration , etc; Col 15, lines 13-15, the migration recommendation could include a scale of increasing difficulty, complexity , cost, or other factor (or combination of factors)). As per claims 10-11, they are computer program product claims of claims 2-3 respectively above. Therefore, they are rejected for the same reasons as claims 2-3 respectively above. As per claims 18-19, they are computing system claims of claims 2-3 respectively above. Therefore, they are rejected for the same reasons as claims 2-3 respectively above. Claims 8, 16 and 24 are rejected under 35 U.S.C. 103 as being unpatentable over BAFNA , Ortiz and KARRI, as applied to claims 6, 14 and 22 respectively above, and further in view of EARNESTY, JR. et al. (US Pub. 2020/0104377 A1) and Zhang et al. (US Patent. 7,831,325 B1). EARNESTY and Zhang were cited in the IDS filed on 08/14/2025. As per claim 8, BAFNA, Ortiz and KARRI teach the invention according to claim 6 above. BAFNA, Ortiz and KARRI fail to specifically teach wherein the project index has a normal value of one and the deviation of the project index above / below the normal value of one is indicative of the increased / decreased level of complexity of the current migration project with respect to a normal migration project. However, EARNESTY teaches wherein the project index has a normal value and the deviation of the project index above / below the normal value is indicative of the increased / decreased level of complexity of the current migration project with respect to a normal migration project ( EARNESTY, [0123] lines 1-14, FIG. 8 illustrates a sample software tool and staffing plan for a data migration according to an embodiment. For example, software tool 800 can include user interface 802, which can display a database classification summary 804, staff summary 806, and migration summary 808. In an embodiment, database classification summary 804 can display the determined source database migration complexity (e.g., very simple , simple , average , complex , very complex , and any other suitable complexity), along with other source database information from the statistical information (e.g., environment, such as production or non-production, source database count, Oracle® GoldenGate database count, and any other suitable information). It would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to have combined the teaching of BAFNA, Ortiz and KARRI with EARNESTY because EARNESTY’s teaching of different level of the complexity would have provided BAFNA, Ortiz and KARRI’s system with the advantage and capability to allow the system to easily identifying the migration complexities based on the different levels in order to improving the system performance and efficiency. BAFNA, Ortiz, KARRI and EARNESTY fail to specifically teach a normal value of one, such that the deviation of the project index above / below the normal value of one is indicative of the increased / decreased level of complexity However, Zhang teaches a normal value of one, such that the deviation of the project index above / below the normal value of one is indicative of the increased / decreased level of complexity (Zhang, Col 8, lines 37-43, the application complexity factor is the ratio of workload complexity and source code efficiency of the target software application to the benchmark application. The performance-related parameters that are monitored by the application trace monitoring task 108 are used to determine whether the assumption of workload complexity and source code efficiency is accurate). It would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to have combined the teaching of BAFNA, Ortiz, KARRI and EARNESTY with Zhang because Zhang’s teaching of application complexity factor is the ratio of workload complexity and source code efficiency of the target software application to the benchmark would have provided BAFNA, Ortiz, KARRI and EARNESTY ’s system with the advantage and capability to allow the system to easily determining the level of complexities which improving the system performance and efficiency. As per claim 16, it is a computer program product claim of claim 8 above. Therefore, it is rejected for the same reason as claim 8 above. As per claim 24, it is a computing system claim of claim 8 above. Therefore, it is rejected for the same reason as claim 8 above. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to FILLIN "Examiner name" \* MERGEFORMAT ZUJIA XU whose telephone number is FILLIN "Phone number" \* MERGEFORMAT (571)272-0954 . The examiner can normally be reached FILLIN "Work Schedule?" \* MERGEFORMAT M-F 9:30-5:30 EST . Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, FILLIN "SPE Name?" \* MERGEFORMAT Aimee J Li can be reached at FILLIN "SPE Phone?" \* MERGEFORMAT (571) 272-4169 . The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. 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